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  "notes": "The chart highlights 'Sustainability' and 'Transparency' as key blind spots.",
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      "text": "More than half of Leaders have also implemented less common practices such as establishment of common databases management of master data quality, and standardization of the system landscape, where they are particularly ahead of others.",
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      "text": "But the evidence suggests that companies are not giving other important attributes enough attention. For example, the sustainability of AI application (its impact on carbon footprint) is a very important attribute for 31 percent of companies, but also a blind spot for 29 percent. Similarly, transparency is a very important attribute for 31 percent of companies, but a blind spot for 28 percent.",
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      "text": "The effective use of AI depends upon robust data management — and this can be a barrier to many companies. Businesses need to establish strong data infrastructure, make sure they are collecting the relevant data needed, and enable it to flow across the organization. Businesses that achieve the 'democratization' of data in this way will be well-placed to proceed on their AI in financial reporting journey.",
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      "text": "Figure 11. Most important attributes for AI adoption, correlated with biggest blind spots",
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